In the bid to make explanations effective for different stakeholders, theories from the social sciences have been explored in line with explainable AI (XAI) [1]}.
Mittelstadt et al. [2]} argued that the risk of conflicts in communicating explanations when the explainer (explanation provider) and the explainee (explanation recipient) have different motives may be mitigated through contrastive, selective, and social explanations. Social in the sense that the explanation process involves different parties and the explainer is able to model the expectations of the explainee. The explanation is selective if it is able to select explanations among several competing hypotheses. It is considered contrastive if it is able to differentiate the properties of two competing hypotheses. Boris Kment [3]} also argued that counterfactuals are helpful in explaining. A counterfactual explanation describes reasons with respect to changes in input that lead to the change of a fact to a foil (competing hypothesis) [1]}.
We investigated our research problems with these theories in mind.
